[HTML][HTML] Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
JX Yu, AM Sieuwerts, Y Zhang, JWM Martens, M Smid… - BMC cancer, 2007 - Springer
JX Yu, AM Sieuwerts, Y Zhang, JWM Martens, M Smid, JGM Klijn, Y Wang, JA Foekens
BMC cancer, 2007•SpringerBackground Published prognostic gene signatures in breast cancer have few genes in
common. Here we provide a rationale for this observation by studying the prognostic power
and the underlying biological pathways of different gene signatures. Methods Gene
signatures to predict the development of metastases in estrogen receptor-positive and
estrogen receptor-negative tumors were identified using 500 re-sampled training sets and
mapping to Gene Ontology Biological Process to identify over-represented pathways. The …
common. Here we provide a rationale for this observation by studying the prognostic power
and the underlying biological pathways of different gene signatures. Methods Gene
signatures to predict the development of metastases in estrogen receptor-positive and
estrogen receptor-negative tumors were identified using 500 re-sampled training sets and
mapping to Gene Ontology Biological Process to identify over-represented pathways. The …
Background
Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.
Methods
Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients.
Results
The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways.
Conclusion
We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.
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